Formula Optimization of 100 mg Chewable Ascorbic Acid Tablets
Bibliographic record
Abstract
Background: Ascorbic acid is a water soluble nigh dose drug that usually degrades in the presence of moisture with the formation of not so biologically active substances.. Pharmaceutical excipients have long been used to impart functionalities that improve stability and enhance patient compliance while increasing cost. Optimization therefore aims at achieving a compromise between a given set of constraints that yields the best formulation. Objectives: The aim of this work is to produce optimised formulation of 100 mg chewable ascorbic acid tablet. Methods: The lubricant was stearic acid at 0.25 %, 0.5% or 0.75 %. The direct compression excipient (DCE) used was Avicel® PH 102 with sorbitol as sweetener in the ratios of sorbitol to Avicel of 1:0, 0:1, 1:1 1:2, 1:3, 1: 4 respectively. The tablet weight was calculated such that the concentration of drug is 30-50% of the direct compression excipient (DCE). A step-wise optimization approach was employed. The best batch was selected as having the highest DCE dilution, hardness ≥4 kgf, minimal tablet defects, and acceptable weight variation, content and content variation. Results: The optimal formula was obtained with the batch that has the following formula, 0.75 % stearic acid at a maximum DCE ratio of avicel: sorbitol of 4 :1 at dilution of 40 % w/w. The flow rate of the powder mix for this batch was 29.70 g/s, with Carr's compressibility index of 22%, and Hausner ratio of 1.28. The angle of o repose determined by free flow from a height of 4 cm was 23 . Drug-excipient compatibility studies using DSC revealed no significant interaction between the tablet components except possible change in crystal structure. Conclusion: The optimal formulation had the following formula: 0.75 % stearic acid, 4 Avicel: 1 sorbitol, and at a maximum DCE dilution of 40% w/w.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".